/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    Apriori.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.associations;

import weka.core.AttributeStats;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

import java.util.ArrayList;
import java.util.Enumeration;
import java.util.Hashtable;
import java.util.List;

/**
 <!-- globalinfo-start -->
 * Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.<br/>
 * The algorithm has an option to mine class association rules. It is adapted as explained in the second reference.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * R. Agrawal, R. Srikant: Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, 478-499, 1994.<br/>
 * <br/>
 * Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining. In: Fourth International Conference on Knowledge Discovery and Data Mining, 80-86, 1998.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;inproceedings{Agrawal1994,
 *    author = {R. Agrawal and R. Srikant},
 *    booktitle = {20th International Conference on Very Large Data Bases},
 *    pages = {478-499},
 *    publisher = {Morgan Kaufmann, Los Altos, CA},
 *    title = {Fast Algorithms for Mining Association Rules in Large Databases},
 *    year = {1994}
 * }
 * 
 * &#64;inproceedings{Liu1998,
 *    author = {Bing Liu and Wynne Hsu and Yiming Ma},
 *    booktitle = {Fourth International Conference on Knowledge Discovery and Data Mining},
 *    pages = {80-86},
 *    publisher = {AAAI Press},
 *    title = {Integrating Classification and Association Rule Mining},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -N &lt;required number of rules output&gt;
 *  The required number of rules. (default = 10)</pre>
 * 
 * <pre> -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
 *  The metric type by which to rank rules. (default = confidence)</pre>
 * 
 * <pre> -C &lt;minimum metric score of a rule&gt;
 *  The minimum confidence of a rule. (default = 0.9)</pre>
 * 
 * <pre> -D &lt;delta for minimum support&gt;
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)</pre>
 * 
 * <pre> -U &lt;upper bound for minimum support&gt;
 *  Upper bound for minimum support. (default = 1.0)</pre>
 * 
 * <pre> -M &lt;lower bound for minimum support&gt;
 *  The lower bound for the minimum support. (default = 0.1)</pre>
 * 
 * <pre> -S &lt;significance level&gt;
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)</pre>
 * 
 * <pre> -I
 *  If set the itemsets found are also output. (default = no)</pre>
 * 
 * <pre> -R
 *  Remove columns that contain all missing values (default = no)</pre>
 * 
 * <pre> -V
 *  Report progress iteratively. (default = no)</pre>
 * 
 * <pre> -A
 *  If set class association rules are mined. (default = no)</pre>
 * 
 * <pre> -c &lt;the class index&gt;
 *  The class index. (default = last)</pre>
 * 
 <!-- options-end -->
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @author Stefan Mutter (mutter@cs.waikato.ac.nz)
 * @version $Revision: 7476 $
 */
public class Apriori 
  extends AbstractAssociator 
  implements OptionHandler, AssociationRulesProducer, 
    CARuleMiner, TechnicalInformationHandler {
  
  /** for serialization */
  static final long serialVersionUID = 3277498842319212687L;
  
  /** The minimum support. */
  protected double m_minSupport;

  /** The upper bound on the support */
  protected double m_upperBoundMinSupport;

  /** The lower bound for the minimum support. */
  protected double m_lowerBoundMinSupport;

  /** Metric type: Confidence */
  protected static final int CONFIDENCE = 0;
  /** Metric type: Lift */
  protected static final int LIFT = 1;
  /** Metric type: Leverage */
  protected static final int LEVERAGE = 2;
  /** Metric type: Conviction */
  protected static final int CONVICTION = 3;
  /** Metric types. */
  public static final Tag [] TAGS_SELECTION = {
    new Tag(CONFIDENCE, "Confidence"),
    new Tag(LIFT, "Lift"),
    new Tag(LEVERAGE, "Leverage"),
    new Tag(CONVICTION, "Conviction")
      };

  /** The selected metric type. */
  protected int m_metricType = CONFIDENCE;

  /** The minimum metric score. */
  protected double m_minMetric;

  /** The maximum number of rules that are output. */
  protected int m_numRules;

  /** Delta by which m_minSupport is decreased in each iteration. */
  protected double m_delta;

  /** Significance level for optional significance test. */
  protected double m_significanceLevel;

  /** Number of cycles used before required number of rules was one. */
  protected int m_cycles;

  /** The set of all sets of itemsets L. */
  protected FastVector m_Ls;

  /** The same information stored in hash tables. */
  protected FastVector m_hashtables;

  /** The list of all generated rules. */
  protected FastVector[] m_allTheRules;

  /** The instances (transactions) to be used for generating 
      the association rules. */
  protected Instances m_instances;

  /** Output itemsets found? */
  protected boolean m_outputItemSets;

  /** Remove columns with all missing values */
  protected boolean m_removeMissingCols;

  /** Report progress iteratively */
  protected boolean m_verbose;
  
  /** Only the class attribute of all Instances.*/
  protected Instances m_onlyClass;
  
  /** The class index. */  
  protected int m_classIndex;
  
  /** Flag indicating whether class association rules are mined. */
  protected boolean m_car;
  
  /** 
   * Treat zeros as missing (rather than a value in their
   * own right)
   */
  protected boolean m_treatZeroAsMissing = false;

  /**
   * Returns a string describing this associator
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Class implementing an Apriori-type algorithm. Iteratively reduces "
      + "the minimum support until it finds the required number of rules with "
      + "the given minimum confidence.\n"
      + "The algorithm has an option to mine class association rules. It is "
      + "adapted as explained in the second reference.\n\n"
      + "For more information see:\n\n"
      + getTechnicalInformation().toString();
  }
  
  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;
    TechnicalInformation 	additional;
    
    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "R. Agrawal and R. Srikant");
    result.setValue(Field.TITLE, "Fast Algorithms for Mining Association Rules in Large Databases");
    result.setValue(Field.BOOKTITLE, "20th International Conference on Very Large Data Bases");
    result.setValue(Field.YEAR, "1994");
    result.setValue(Field.PAGES, "478-499");
    result.setValue(Field.PUBLISHER, "Morgan Kaufmann, Los Altos, CA");

    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR, "Bing Liu and Wynne Hsu and Yiming Ma");
    additional.setValue(Field.TITLE, "Integrating Classification and Association Rule Mining");
    additional.setValue(Field.BOOKTITLE, "Fourth International Conference on Knowledge Discovery and Data Mining");
    additional.setValue(Field.YEAR, "1998");
    additional.setValue(Field.PAGES, "80-86");
    additional.setValue(Field.PUBLISHER, "AAAI Press");
    
    return result;
  }

  /**
   * Constructor that allows to sets default values for the 
   * minimum confidence and the maximum number of rules
   * the minimum confidence.
   */
  public Apriori() {

    resetOptions();
  }

  /**
   * Resets the options to the default values.
   */
  public void resetOptions() {
    
    m_removeMissingCols = false;
    m_verbose = false;
    m_delta = 0.05;
    m_minMetric = 0.90;
    m_numRules = 10;
    m_lowerBoundMinSupport = 0.1;
    m_upperBoundMinSupport = 1.0;
    m_significanceLevel = -1;
    m_outputItemSets = false;
    m_car = false;
    m_classIndex = -1;
  }

  /**
   * Removes columns that are all missing from the data
   * @param instances the instances
   * @return a new set of instances with all missing columns removed
   * @throws Exception if something goes wrong
   */
  protected Instances removeMissingColumns(Instances instances) 
    throws Exception {
    
    int numInstances = instances.numInstances();
    StringBuffer deleteString = new StringBuffer();
    int removeCount = 0;
    boolean first = true;
    int maxCount = 0;
    
    for (int i=0;i<instances.numAttributes();i++) {
      AttributeStats as = instances.attributeStats(i);
      if (m_upperBoundMinSupport == 1.0 && maxCount != numInstances) {
	// see if we can decrease this by looking for the most frequent value
	int [] counts = as.nominalCounts;
	if (counts[Utils.maxIndex(counts)] > maxCount) {
	  maxCount = counts[Utils.maxIndex(counts)];
	}
      }
      if (as.missingCount == numInstances) {
	if (first) {
	  deleteString.append((i+1));
	  first = false;
	} else {
	  deleteString.append(","+(i+1));
	}
	removeCount++;
      }
    }
    if (m_verbose) {
      System.err.println("Removed : "+removeCount+" columns with all missing "
			 +"values.");
    }
    if (m_upperBoundMinSupport == 1.0 && maxCount != numInstances) {
      m_upperBoundMinSupport = (double)maxCount / (double)numInstances;
      if (m_verbose) {
	System.err.println("Setting upper bound min support to : "
			   +m_upperBoundMinSupport);
      }
    }

    if (deleteString.toString().length() > 0) {
      Remove af = new Remove();
      af.setAttributeIndices(deleteString.toString());
      af.setInvertSelection(false);
      af.setInputFormat(instances);
      Instances newInst = Filter.useFilter(instances, af);

      return newInst;
    }
    return instances;
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    // enable what we can handle
    
    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class (can handle a nominal class if CAR rules are selected). This
    result.enable(Capability.NO_CLASS);
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);
    
    return result;
  }

  /**
   * Method that generates all large itemsets with a minimum support, and from
   * these all association rules with a minimum confidence.
   *
   * @param instances the instances to be used for generating the associations
   * @throws Exception if rules can't be built successfully
   */
  public void buildAssociations(Instances instances) throws Exception {

    double[] confidences, supports;
    int[] indices;
    FastVector[] sortedRuleSet;
    double necSupport=0;

    instances = new Instances(instances);
    
    if (m_removeMissingCols) {
      instances = removeMissingColumns(instances);
    }
    if(m_car && m_metricType != CONFIDENCE)
      throw new Exception("For CAR-Mining metric type has to be confidence!");
    
    // only set class index if CAR is requested
    if (m_car) {
      if (m_classIndex == -1 ) {
        instances.setClassIndex(instances.numAttributes()-1);     
      } else if (m_classIndex <= instances.numAttributes() && m_classIndex > 0) {
        instances.setClassIndex(m_classIndex - 1);
      } else {
        throw new Exception("Invalid class index.");
      }
    }

    // can associator handle the data?
    getCapabilities().testWithFail(instances);

    m_cycles = 0;
    
    // make sure that the lower bound is equal to at least one instance
    double lowerBoundMinSupportToUse = 
      (m_lowerBoundMinSupport * (double)instances.numInstances() < 1.0)
      ? 1.0 / (double)instances.numInstances()
          : m_lowerBoundMinSupport;
    
    if(m_car){
        //m_instances does not contain the class attribute
        m_instances = LabeledItemSet.divide(instances,false);
    
        //m_onlyClass contains only the class attribute
        m_onlyClass = LabeledItemSet.divide(instances,true);
    }
    else
        m_instances = instances;
    
    if(m_car && m_numRules == Integer.MAX_VALUE){
        // Set desired minimum support
        m_minSupport = lowerBoundMinSupportToUse;
    }
    else{
        // Decrease minimum support until desired number of rules found.
        //m_minSupport = m_upperBoundMinSupport - m_delta;
        m_minSupport = 1.0 - m_delta;
        m_minSupport = (m_minSupport < lowerBoundMinSupportToUse) 
            ? lowerBoundMinSupportToUse 
            : m_minSupport;
    }

    do {
      // Reserve space for variables
      m_Ls = new FastVector();
      m_hashtables = new FastVector();
      m_allTheRules = new FastVector[6];
      m_allTheRules[0] = new FastVector();
      m_allTheRules[1] = new FastVector();
      m_allTheRules[2] = new FastVector();
      //if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
	m_allTheRules[3] = new FastVector();
	m_allTheRules[4] = new FastVector();
	m_allTheRules[5] = new FastVector();
     // }
      sortedRuleSet = new FastVector[6];
      sortedRuleSet[0] = new FastVector();
      sortedRuleSet[1] = new FastVector();
      sortedRuleSet[2] = new FastVector();
      //if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
	sortedRuleSet[3] = new FastVector();
	sortedRuleSet[4] = new FastVector();
	sortedRuleSet[5] = new FastVector();
      //}
      if(!m_car){
        // Find large itemsets and rules
        findLargeItemSets();
        if (m_significanceLevel != -1 || m_metricType != CONFIDENCE) 
            findRulesBruteForce();
        else
            findRulesQuickly();
      }
      else{
          findLargeCarItemSets();
          findCarRulesQuickly();
      }
      
      // prune rules for upper bound min support
      if (m_upperBoundMinSupport < 1.0) {
        pruneRulesForUpperBoundSupport();
      }
      
      // Sort rules according to their support
      /*supports = new double[m_allTheRules[2].size()];
      for (int i = 0; i < m_allTheRules[2].size(); i++) 
	supports[i] = (double)((AprioriItemSet)m_allTheRules[1].elementAt(i)).support();
      indices = Utils.stableSort(supports);
      for (int i = 0; i < m_allTheRules[2].size(); i++) {
	sortedRuleSet[0].addElement(m_allTheRules[0].elementAt(indices[i]));
	sortedRuleSet[1].addElement(m_allTheRules[1].elementAt(indices[i]));
	sortedRuleSet[2].addElement(m_allTheRules[2].elementAt(indices[i]));
	if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
	  sortedRuleSet[3].addElement(m_allTheRules[3].elementAt(indices[i]));
	  sortedRuleSet[4].addElement(m_allTheRules[4].elementAt(indices[i]));
	  sortedRuleSet[5].addElement(m_allTheRules[5].elementAt(indices[i]));
	}
      }*/
      int j = m_allTheRules[2].size()-1;
      supports = new double[m_allTheRules[2].size()];
      for (int i = 0; i < (j+1); i++) 
	supports[j-i] = ((double)((ItemSet)m_allTheRules[1].elementAt(j-i)).support())*(-1);
      indices = Utils.stableSort(supports);
      for (int i = 0; i < (j+1); i++) {
	sortedRuleSet[0].addElement(m_allTheRules[0].elementAt(indices[j-i]));
	sortedRuleSet[1].addElement(m_allTheRules[1].elementAt(indices[j-i]));
	sortedRuleSet[2].addElement(m_allTheRules[2].elementAt(indices[j-i]));
	if (!m_car) {
	//if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
	  sortedRuleSet[3].addElement(m_allTheRules[3].elementAt(indices[j-i]));
	  sortedRuleSet[4].addElement(m_allTheRules[4].elementAt(indices[j-i]));
	  sortedRuleSet[5].addElement(m_allTheRules[5].elementAt(indices[j-i]));
	}
	//}
      }

      // Sort rules according to their confidence
      m_allTheRules[0].removeAllElements();
      m_allTheRules[1].removeAllElements();
      m_allTheRules[2].removeAllElements();
      //if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
	m_allTheRules[3].removeAllElements();
	m_allTheRules[4].removeAllElements();
	m_allTheRules[5].removeAllElements();
      //}
      confidences = new double[sortedRuleSet[2].size()];
      int sortType = 2 + m_metricType;

      for (int i = 0; i < sortedRuleSet[2].size(); i++) 
	confidences[i] = 
	  ((Double)sortedRuleSet[sortType].elementAt(i)).doubleValue();
      indices = Utils.stableSort(confidences);
      for (int i = sortedRuleSet[0].size() - 1; 
	   (i >= (sortedRuleSet[0].size() - m_numRules)) && (i >= 0); i--) {
	m_allTheRules[0].addElement(sortedRuleSet[0].elementAt(indices[i]));
	m_allTheRules[1].addElement(sortedRuleSet[1].elementAt(indices[i]));
	m_allTheRules[2].addElement(sortedRuleSet[2].elementAt(indices[i]));
	//if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
	if (!m_car) {
	  m_allTheRules[3].addElement(sortedRuleSet[3].elementAt(indices[i]));
	  m_allTheRules[4].addElement(sortedRuleSet[4].elementAt(indices[i]));
	  m_allTheRules[5].addElement(sortedRuleSet[5].elementAt(indices[i]));
	}
	//}
      }

      if (m_verbose) {
	if (m_Ls.size() > 1) {
	  System.out.println(toString());
	}		
      }
      
      
      if(m_minSupport == lowerBoundMinSupportToUse || m_minSupport - m_delta >  lowerBoundMinSupportToUse)
        m_minSupport -= m_delta;
      else
        m_minSupport = lowerBoundMinSupportToUse;
      
      
      necSupport = Math.rint(m_minSupport * (double)m_instances.numInstances());      

      m_cycles++;
    } while ((m_allTheRules[0].size() < m_numRules) &&
	     (Utils.grOrEq(m_minSupport, lowerBoundMinSupportToUse))
	     /*	     (necSupport >= lowerBoundNumInstancesSupport)*/
	     /*	     (Utils.grOrEq(m_minSupport, m_lowerBoundMinSupport)) */ &&     
	     (necSupport >= 1));
    m_minSupport += m_delta;
  }

  private void pruneRulesForUpperBoundSupport() {
    int necMaxSupport = (int)(m_upperBoundMinSupport * (double)m_instances.numInstances()+0.5);
    
    FastVector[] prunedRules = new FastVector[6];
    for (int i = 0; i < 6; i++) {
      prunedRules[i] = new FastVector();
    }
    
    for (int i = 0; i < m_allTheRules[0].size(); i++) {
      if (((ItemSet)m_allTheRules[1].elementAt(i)).support() <= necMaxSupport) {
        prunedRules[0].addElement(m_allTheRules[0].elementAt(i));
        prunedRules[1].addElement(m_allTheRules[1].elementAt(i));
        prunedRules[2].addElement(m_allTheRules[2].elementAt(i));
        
        if (!m_car) {
          prunedRules[3].addElement(m_allTheRules[3].elementAt(i));
          prunedRules[4].addElement(m_allTheRules[4].elementAt(i));
          prunedRules[5].addElement(m_allTheRules[5].elementAt(i));
        }        
      }
    }
    m_allTheRules[0] = prunedRules[0];
    m_allTheRules[1] = prunedRules[1];
    m_allTheRules[2] = prunedRules[2];
    m_allTheRules[3] = prunedRules[3];
    m_allTheRules[4] = prunedRules[4];
    m_allTheRules[5] = prunedRules[5];

  }
  
  
      /**
     * Method that mines all class association rules with minimum support and
     * with a minimum confidence.
     * @return an sorted array of FastVector (confidence depended) containing the rules and metric information
     * @param data the instances for which class association rules should be mined
     * @throws Exception if rules can't be built successfully
     */
    public FastVector[] mineCARs(Instances data) throws Exception{
	 
        m_car = true;
	buildAssociations(data);
	return m_allTheRules;
    }

   /**
   * Gets the instances without the class atrribute.
   *
   * @return the instances without the class attribute.
   */ 
  public Instances getInstancesNoClass() {
      
      return m_instances;
  }  
  
  
  /**
   * Gets only the class attribute of the instances.
   *
   * @return the class attribute of all instances.
   */ 
  public Instances getInstancesOnlyClass() {
      
      return m_onlyClass;
  }  


  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    String string1 = "\tThe required number of rules. (default = " + m_numRules + ")",
      string2 = 
      "\tThe minimum confidence of a rule. (default = " + m_minMetric + ")",
      string3 = "\tThe delta by which the minimum support is decreased in\n",
      string4 = "\teach iteration. (default = " + m_delta + ")",
      string5 = 
      "\tThe lower bound for the minimum support. (default = " + 
      m_lowerBoundMinSupport + ")",
      string6 = "\tIf used, rules are tested for significance at\n",
      string7 = "\tthe given level. Slower. (default = no significance testing)",
      string8 = "\tIf set the itemsets found are also output. (default = no)",
      string9 = "\tIf set class association rules are mined. (default = no)",
      string10 = "\tThe class index. (default = last)",
      stringType = "\tThe metric type by which to rank rules. (default = "
      +"confidence)",
      stringZeroAsMissing = "\tTreat zero (i.e. first value of nominal attributes) as " +
      		"missing";
    

    FastVector newVector = new FastVector(11);

    newVector.addElement(new Option(string1, "N", 1, 
				    "-N <required number of rules output>"));
    newVector.addElement(new Option(stringType, "T", 1,
				    "-T <0=confidence | 1=lift | "
				    +"2=leverage | 3=Conviction>"));
    newVector.addElement(new Option(string2, "C", 1, 
				    "-C <minimum metric score of a rule>"));
    newVector.addElement(new Option(string3 + string4, "D", 1,
				    "-D <delta for minimum support>"));
    newVector.addElement(new Option("\tUpper bound for minimum support. "
				    +"(default = 1.0)", "U", 1,
				     "-U <upper bound for minimum support>"));
    newVector.addElement(new Option(string5, "M", 1,
				    "-M <lower bound for minimum support>"));
    newVector.addElement(new Option(string6 + string7, "S", 1,
				    "-S <significance level>"));
    newVector.addElement(new Option(string8, "I", 0,
				    "-I"));
    newVector.addElement(new Option("\tRemove columns that contain "
				    +"all missing values (default = no)"
				    , "R", 0,
				    "-R"));
    newVector.addElement(new Option("\tReport progress iteratively. (default "
				    +"= no)", "V", 0,
				    "-V"));
    newVector.addElement(new Option(string9, "A", 0,
				    "-A"));
    newVector.addElement(new Option(stringZeroAsMissing, "Z", 0,
        "-Z"));
    newVector.addElement(new Option(string10, "c", 1,
				    "-c <the class index>"));
    
    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -N &lt;required number of rules output&gt;
   *  The required number of rules. (default = 10)</pre>
   * 
   * <pre> -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
   *  The metric type by which to rank rules. (default = confidence)</pre>
   * 
   * <pre> -C &lt;minimum metric score of a rule&gt;
   *  The minimum confidence of a rule. (default = 0.9)</pre>
   * 
   * <pre> -D &lt;delta for minimum support&gt;
   *  The delta by which the minimum support is decreased in
   *  each iteration. (default = 0.05)</pre>
   * 
   * <pre> -U &lt;upper bound for minimum support&gt;
   *  Upper bound for minimum support. (default = 1.0)</pre>
   * 
   * <pre> -M &lt;lower bound for minimum support&gt;
   *  The lower bound for the minimum support. (default = 0.1)</pre>
   * 
   * <pre> -S &lt;significance level&gt;
   *  If used, rules are tested for significance at
   *  the given level. Slower. (default = no significance testing)</pre>
   * 
   * <pre> -I
   *  If set the itemsets found are also output. (default = no)</pre>
   * 
   * <pre> -R
   *  Remove columns that contain all missing values (default = no)</pre>
   * 
   * <pre> -V
   *  Report progress iteratively. (default = no)</pre>
   * 
   * <pre> -A
   *  If set class association rules are mined. (default = no)</pre>
   * 
   * <pre> -c &lt;the class index&gt;
   *  The class index. (default = last)</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported 
   */
  public void setOptions(String[] options) throws Exception {
    
    resetOptions();
    String numRulesString = Utils.getOption('N', options),
      minConfidenceString = Utils.getOption('C', options),
      deltaString = Utils.getOption('D', options),
      maxSupportString = Utils.getOption('U', options),
      minSupportString = Utils.getOption('M', options),
      significanceLevelString = Utils.getOption('S', options),
      classIndexString = Utils.getOption('c',options);
    
    String metricTypeString = Utils.getOption('T', options);
    if (metricTypeString.length() != 0) {
      setMetricType(new SelectedTag(Integer.parseInt(metricTypeString),
				    TAGS_SELECTION));
    }
    
    if (numRulesString.length() != 0) {
      m_numRules = Integer.parseInt(numRulesString);
    }
    if (classIndexString.length() != 0) {
      if (classIndexString.equalsIgnoreCase("last")) {
        m_classIndex = -1;
      } else if (classIndexString.equalsIgnoreCase("first")) {
        m_classIndex = 0;
      } else {
        m_classIndex = Integer.parseInt(classIndexString);
      }
    }
    if (minConfidenceString.length() != 0) {
      m_minMetric = (new Double(minConfidenceString)).doubleValue();
    }
    if (deltaString.length() != 0) {
      m_delta = (new Double(deltaString)).doubleValue();
    }
    if (maxSupportString.length() != 0) {
      setUpperBoundMinSupport((new Double(maxSupportString)).doubleValue());
    }
    if (minSupportString.length() != 0) {
      m_lowerBoundMinSupport = (new Double(minSupportString)).doubleValue();
    }
    if (significanceLevelString.length() != 0) {
      m_significanceLevel = (new Double(significanceLevelString)).doubleValue();
    }
    m_outputItemSets = Utils.getFlag('I', options);
    m_car = Utils.getFlag('A', options);
    m_verbose = Utils.getFlag('V', options);
    m_treatZeroAsMissing = Utils.getFlag('Z', options);
    
    setRemoveAllMissingCols(Utils.getFlag('R', options));
  }

  /**
   * Gets the current settings of the Apriori object.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {

    String [] options = new String [21];
    int current = 0;

    if (m_outputItemSets) {
      options[current++] = "-I";
    }

    if (getRemoveAllMissingCols()) {
      options[current++] = "-R";
    }

    options[current++] = "-N"; options[current++] = "" + m_numRules;
    options[current++] = "-T"; options[current++] = "" + m_metricType;
    options[current++] = "-C"; options[current++] = "" + m_minMetric;
    options[current++] = "-D"; options[current++] = "" + m_delta;
    options[current++] = "-U"; options[current++] = "" + m_upperBoundMinSupport;
    options[current++] = "-M"; options[current++] = "" + m_lowerBoundMinSupport;
    options[current++] = "-S"; options[current++] = "" + m_significanceLevel;
    if (m_car)
      options[current++] = "-A";
    if (m_verbose)
      options[current++] = "-V";
    
    if (m_treatZeroAsMissing) {
      options[current++] = "-Z";
    }
    options[current++] = "-c"; options[current++] = "" + m_classIndex;
    
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Outputs the size of all the generated sets of itemsets and the rules.
   * 
   * @return a string representation of the model
   */
  public String toString() {

    StringBuffer text = new StringBuffer();

    if (m_Ls.size() <= 1)
      return "\nNo large itemsets and rules found!\n";
    text.append("\nApriori\n=======\n\n");
    text.append("Minimum support: " 
		+ Utils.doubleToString(m_minSupport,2) 
		+ " (" + ((int)(m_minSupport * (double)m_instances.numInstances()+0.5)) 
		+ " instances)"
		+ '\n');
    text.append("Minimum metric <");
    switch(m_metricType) {
    case CONFIDENCE:
      text.append("confidence>: ");
      break;
    case LIFT:
      text.append("lift>: ");
      break;
    case LEVERAGE:
      text.append("leverage>: ");
      break;
    case CONVICTION:
      text.append("conviction>: ");
      break;
    }
    text.append(Utils.doubleToString(m_minMetric,2)+'\n');
   
    if (m_significanceLevel != -1)
      text.append("Significance level: "+
		  Utils.doubleToString(m_significanceLevel,2)+'\n');
    text.append("Number of cycles performed: " + m_cycles+'\n');
    text.append("\nGenerated sets of large itemsets:\n");
    if(!m_car){
        for (int i = 0; i < m_Ls.size(); i++) {
            text.append("\nSize of set of large itemsets L("+(i+1)+"): "+
		  ((FastVector)m_Ls.elementAt(i)).size()+'\n');
            if (m_outputItemSets) {
                text.append("\nLarge Itemsets L("+(i+1)+"):\n");
                for (int j = 0; j < ((FastVector)m_Ls.elementAt(i)).size(); j++)
                    text.append(((AprioriItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)).
		      toString(m_instances)+"\n");
            }
        }
        text.append("\nBest rules found:\n\n");
        for (int i = 0; i < m_allTheRules[0].size(); i++) {
            text.append(Utils.doubleToString((double)i+1, 
		  (int)(Math.log(m_numRules)/Math.log(10)+1),0)+
		  ". " + ((AprioriItemSet)m_allTheRules[0].elementAt(i)).
		  toString(m_instances) 
		  + " ==> " + ((AprioriItemSet)m_allTheRules[1].elementAt(i)).
		  toString(m_instances));
                  text.append("    " + ((m_metricType == CONFIDENCE) ? "<" : "") 
                      + "conf:(" + Utils.doubleToString(((Double)m_allTheRules[2].
                          elementAt(i)).doubleValue(),2)+")" 
                          + ((m_metricType == CONFIDENCE) ? ">" : ""));   
		  
            //if (/*m_metricType != CONFIDENCE ||*/ m_significanceLevel != -1) {
                text.append((m_metricType == LIFT ? " <" : "")+" lift:("+  
		    Utils.doubleToString(((Double)m_allTheRules[3].
					  elementAt(i)).doubleValue(),2)
		    +")"+(m_metricType == LIFT ? ">" : ""));
                text.append((m_metricType == LEVERAGE ? " <" : "")+" lev:("+  
		    Utils.doubleToString(((Double)m_allTheRules[4].
					  elementAt(i)).doubleValue(),2)
		    +")");
                text.append(" ["+
		    (int)(((Double)m_allTheRules[4].elementAt(i))
			  .doubleValue() * (double)m_instances.numInstances())
		    +"]"+(m_metricType == LEVERAGE ? ">" : ""));
                text.append((m_metricType == CONVICTION ? " <" : "")+" conv:("+  
		    Utils.doubleToString(((Double)m_allTheRules[5].
					  elementAt(i)).doubleValue(),2)
		    +")"+(m_metricType == CONVICTION ? ">" : ""));
            //}
            text.append('\n');
        }
    }
    else{
        for (int i = 0; i < m_Ls.size(); i++) {
            text.append("\nSize of set of large itemsets L("+(i+1)+"): "+
		  ((FastVector)m_Ls.elementAt(i)).size()+'\n');
            if (m_outputItemSets) {
                text.append("\nLarge Itemsets L("+(i+1)+"):\n");
                for (int j = 0; j < ((FastVector)m_Ls.elementAt(i)).size(); j++){
                    text.append(((ItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)).
		      toString(m_instances)+"\n");
                    text.append(((LabeledItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)).m_classLabel+"  ");
                    text.append(((LabeledItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)).support()+"\n");
                }
            }
        }
        text.append("\nBest rules found:\n\n");
        for (int i = 0; i < m_allTheRules[0].size(); i++) {
            text.append(Utils.doubleToString((double)i+1, 
					     (int)(Math.log(m_numRules)/Math.log(10)+1),0)+
			". " + ((ItemSet)m_allTheRules[0].elementAt(i)).
			toString(m_instances) 
			+ " ==> " + ((ItemSet)m_allTheRules[1].elementAt(i)).
			toString(m_onlyClass) +"    conf:("+  
			Utils.doubleToString(((Double)m_allTheRules[2].
					      elementAt(i)).doubleValue(),2)+")");
	
            text.append('\n');
        }
    }
        
    return text.toString();
  }
  
   /**
   * Returns the metric string for the chosen metric type
   * @return a string describing the used metric for the interestingness of a class association rule
   */
  public String metricString() {
      
        switch(m_metricType) {
	case LIFT:
	    return "lif";
	case LEVERAGE:
	    return "leverage"; 
	case CONVICTION:
	    return "conviction";
        default:
            return "conf";
	}
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String removeAllMissingColsTipText() {
    return "Remove columns with all missing values.";
  }

  /**
   * Remove columns containing all missing values.
   * @param r true if cols are to be removed.
   */
  public void setRemoveAllMissingCols(boolean r) {
    m_removeMissingCols = r;
  }

  /**
   * Returns whether columns containing all missing values are to be removed
   * @return true if columns are to be removed.
   */
  public boolean getRemoveAllMissingCols() {
    return m_removeMissingCols;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String upperBoundMinSupportTipText() {
    return "Upper bound for minimum support. Start iteratively decreasing "
      +"minimum support from this value.";
  }

  /**
   * Get the value of upperBoundMinSupport.
   *
   * @return Value of upperBoundMinSupport.
   */
  public double getUpperBoundMinSupport() {
    
    return m_upperBoundMinSupport;
  }
  
  /**
   * Set the value of upperBoundMinSupport.
   *
   * @param v  Value to assign to upperBoundMinSupport.
   */
  public void setUpperBoundMinSupport(double v) {
    
    m_upperBoundMinSupport = v;
  }

   /**
   * Sets the class index
   * @param index the class index
   */  
  public void setClassIndex(int index){
      
      m_classIndex = index;
  }
  
  /**
   * Gets the class index
   * @return the index of the class attribute
   */  
  public int getClassIndex(){
      
      return m_classIndex;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String classIndexTipText() {
    return "Index of the class attribute. If set to -1, the last attribute is taken as class attribute.";

  }

  /**
   * Sets class association rule mining
   * @param flag if class association rules are mined, false otherwise
   */  
  public void setCar(boolean flag){
      m_car = flag;
  }
  
  /**
   * Gets whether class association ruels are mined
   * @return true if class association rules are mined, false otherwise
   */  
  public boolean getCar(){
      return m_car;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String carTipText() {
    return "If enabled class association rules are mined instead of (general) association rules.";
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String lowerBoundMinSupportTipText() {
    return "Lower bound for minimum support.";
  }

  /**
   * Get the value of lowerBoundMinSupport.
   *
   * @return Value of lowerBoundMinSupport.
   */
  public double getLowerBoundMinSupport() {
    
    return m_lowerBoundMinSupport;
  }
  
  /**
   * Set the value of lowerBoundMinSupport.
   *
   * @param v  Value to assign to lowerBoundMinSupport.
   */
  public void setLowerBoundMinSupport(double v) {
    
    m_lowerBoundMinSupport = v;
  }
  
  /**
   * Get the metric type
   *
   * @return the type of metric to use for ranking rules
   */
  public SelectedTag getMetricType() {
    return new SelectedTag(m_metricType, TAGS_SELECTION);
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String metricTypeTipText() {
    return "Set the type of metric by which to rank rules. Confidence is "
      +"the proportion of the examples covered by the premise that are also "
      +"covered by the consequence (Class association rules can only be mined using confidence). Lift is confidence divided by the "
      +"proportion of all examples that are covered by the consequence. This "
      +"is a measure of the importance of the association that is independent "
      +"of support. Leverage is the proportion of additional examples covered "
      +"by both the premise and consequence above those expected if the "
      +"premise and consequence were independent of each other. The total "
      +"number of examples that this represents is presented in brackets "
      +"following the leverage. Conviction is "
      +"another measure of departure from independence. Conviction is given "
      +"by P(premise)P(!consequence) / P(premise, !consequence).";
  }

  /**
   * Set the metric type for ranking rules
   *
   * @param d the type of metric
   */
  public void setMetricType (SelectedTag d) {
    
    if (d.getTags() == TAGS_SELECTION) {
      m_metricType = d.getSelectedTag().getID();
    }

    if (m_significanceLevel != -1 && m_metricType != CONFIDENCE) {
      m_metricType = CONFIDENCE;
    }

    if (m_metricType == CONFIDENCE) {
      setMinMetric(0.9);
    }

    if (m_metricType == LIFT || m_metricType == CONVICTION) {
      setMinMetric(1.1);
    }
  
    if (m_metricType == LEVERAGE) {
      setMinMetric(0.1);
    }
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minMetricTipText() {
    return "Minimum metric score. Consider only rules with scores higher than "
      +"this value.";
  }

  /**
   * Get the value of minConfidence.
   *
   * @return Value of minConfidence.
   */
  public double getMinMetric() {
    
    return m_minMetric;
  }
  
  /**
   * Set the value of minConfidence.
   *
   * @param v  Value to assign to minConfidence.
   */
  public void setMinMetric(double v) {
    
    m_minMetric = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numRulesTipText() {
    return "Number of rules to find.";
  }

  /**
   * Get the value of numRules.
   *
   * @return Value of numRules.
   */
  public int getNumRules() {
    
    return m_numRules;
  }
  
  /**
   * Set the value of numRules.
   *
   * @param v  Value to assign to numRules.
   */
  public void setNumRules(int v) {
    
    m_numRules = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String deltaTipText() {
    return "Iteratively decrease support by this factor. Reduces support "
      +"until min support is reached or required number of rules has been "
      +"generated.";
  }
    
  /**
   * Get the value of delta.
   *
   * @return Value of delta.
   */
  public double getDelta() {
    
    return m_delta;
  }
  
  /**
   * Set the value of delta.
   *
   * @param v  Value to assign to delta.
   */
  public void setDelta(double v) {
    
    m_delta = v;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String significanceLevelTipText() {
    return "Significance level. Significance test (confidence metric only).";
  }

  /**
   * Get the value of significanceLevel.
   *
   * @return Value of significanceLevel.
   */
  public double getSignificanceLevel() {
    
    return m_significanceLevel;
  }
  
  /**
   * Set the value of significanceLevel.
   *
   * @param v  Value to assign to significanceLevel.
   */
  public void setSignificanceLevel(double v) {
    
    m_significanceLevel = v;
  }

  /**
   * Sets whether itemsets are output as well
   * @param flag true if itemsets are to be output as well
   */  
  public void setOutputItemSets(boolean flag){
    m_outputItemSets = flag;
  }
  
  /**
   * Gets whether itemsets are output as well
   * @return true if itemsets are output as well
   */  
  public boolean getOutputItemSets(){
    return m_outputItemSets;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String outputItemSetsTipText() {
    return "If enabled the itemsets are output as well.";
  }

  /**
   * Sets verbose mode
   * @param flag true if algorithm should be run in verbose mode
   */  
  public void setVerbose(boolean flag){
    m_verbose = flag;
  }
  
  /**
   * Gets whether algorithm is run in verbose mode
   * @return true if algorithm is run in verbose mode
   */  
  public boolean getVerbose(){
    return m_verbose;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String verboseTipText() {
    return "If enabled the algorithm will be run in verbose mode.";
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String treatZeroAsMissingTipText() {
    return "If enabled, zero (that is, the first value of a nominal) is "
    + "treated in the same way as a missing value.";
  }
  
  /**
   * Sets whether zeros (i.e. the first value of a nominal attribute)
   * should be treated as missing values.
   * 
   * @param z true if zeros should be treated as missing values.
   */
  public void setTreatZeroAsMissing(boolean z) {
    m_treatZeroAsMissing = z;
  }
  
  /**
   * Gets whether zeros (i.e. the first value of a nominal attribute)
   * is to be treated int he same way as missing values.
   * 
   * @return true if zeros are to be treated like missing values.
   */
  public boolean getTreatZeroAsMissing() {
    return m_treatZeroAsMissing;
  }

  /** 
   * Method that finds all large itemsets for the given set of instances.
   *
   * @throws Exception if an attribute is numeric
   */
  private void findLargeItemSets() throws Exception {
    
    FastVector kMinusOneSets, kSets;
    Hashtable hashtable;
    int necSupport, necMaxSupport,i = 0;
    
    
    
    // Find large itemsets

    // minimum support
    necSupport = (int)(m_minSupport * (double)m_instances.numInstances()+0.5);
    necMaxSupport = (int)(m_upperBoundMinSupport * (double)m_instances.numInstances()+0.5);
   
    kSets = AprioriItemSet.singletons(m_instances, m_treatZeroAsMissing);
    AprioriItemSet.upDateCounters(kSets,m_instances);
    kSets = AprioriItemSet.deleteItemSets(kSets, necSupport, m_instances.numInstances());
    if (kSets.size() == 0)
      return;
    do {
      m_Ls.addElement(kSets);
      kMinusOneSets = kSets;
      kSets = AprioriItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances());
      hashtable = AprioriItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size());
      m_hashtables.addElement(hashtable);
      kSets = AprioriItemSet.pruneItemSets(kSets, hashtable);
      AprioriItemSet.upDateCounters(kSets, m_instances);
      kSets = AprioriItemSet.deleteItemSets(kSets, necSupport, m_instances.numInstances());
      i++;
    } while (kSets.size() > 0);
  }  

  /** 
   * Method that finds all association rules and performs significance test.
   *
   * @throws Exception if an attribute is numeric
   */
  private void findRulesBruteForce() throws Exception {

    FastVector[] rules;

    // Build rules
    for (int j = 1; j < m_Ls.size(); j++) {
      FastVector currentItemSets = (FastVector)m_Ls.elementAt(j);
      Enumeration enumItemSets = currentItemSets.elements();
      while (enumItemSets.hasMoreElements()) {
	AprioriItemSet currentItemSet = (AprioriItemSet)enumItemSets.nextElement();
        //AprioriItemSet currentItemSet = new AprioriItemSet((ItemSet)enumItemSets.nextElement());
	rules=currentItemSet.generateRulesBruteForce(m_minMetric,m_metricType,
				  m_hashtables,j+1,
				  m_instances.numInstances(),
				  m_significanceLevel);
	for (int k = 0; k < rules[0].size(); k++) {
	  m_allTheRules[0].addElement(rules[0].elementAt(k));
	  m_allTheRules[1].addElement(rules[1].elementAt(k));
	  m_allTheRules[2].addElement(rules[2].elementAt(k));

	  m_allTheRules[3].addElement(rules[3].elementAt(k));
	  m_allTheRules[4].addElement(rules[4].elementAt(k));
	  m_allTheRules[5].addElement(rules[5].elementAt(k));
	}
      }
    }
  }

  /** 
   * Method that finds all association rules.
   *
   * @throws Exception if an attribute is numeric
   */
  private void findRulesQuickly() throws Exception {

    FastVector[] rules;
    // Build rules
    for (int j = 1; j < m_Ls.size(); j++) {
      FastVector currentItemSets = (FastVector)m_Ls.elementAt(j);
      Enumeration enumItemSets = currentItemSets.elements();
      while (enumItemSets.hasMoreElements()) {
	AprioriItemSet currentItemSet = (AprioriItemSet)enumItemSets.nextElement();
        //AprioriItemSet currentItemSet = new AprioriItemSet((ItemSet)enumItemSets.nextElement());
	rules = currentItemSet.generateRules(m_minMetric, m_hashtables, j + 1);
	for (int k = 0; k < rules[0].size(); k++) {
	  m_allTheRules[0].addElement(rules[0].elementAt(k));
	  m_allTheRules[1].addElement(rules[1].elementAt(k));
	  m_allTheRules[2].addElement(rules[2].elementAt(k));
	  
	  if (rules.length > 3) {
	    m_allTheRules[3].addElement(rules[3].elementAt(k));
	    m_allTheRules[4].addElement(rules[4].elementAt(k));
	    m_allTheRules[5].addElement(rules[5].elementAt(k));
	  }
	}
      }
    }
  }
  
      /**
     *
     * Method that finds all large itemsets for class association rules for the given set of instances.
     * @throws Exception if an attribute is numeric
     */
    private void findLargeCarItemSets() throws Exception {
	
	FastVector kMinusOneSets, kSets;
	Hashtable hashtable;
	int necSupport, necMaxSupport,i = 0;
	
	// Find large itemsets
	
	// minimum support
        double nextMinSupport = m_minSupport*(double)m_instances.numInstances();
        double nextMaxSupport = m_upperBoundMinSupport*(double)m_instances.numInstances();
	if((double)Math.rint(nextMinSupport) == nextMinSupport){
            necSupport = (int) nextMinSupport;
        }
        else{
            necSupport = Math.round((float)(nextMinSupport+0.5));
        }
        if((double)Math.rint(nextMaxSupport) == nextMaxSupport){
            necMaxSupport = (int) nextMaxSupport;
        }
        else{
            necMaxSupport = Math.round((float)(nextMaxSupport+0.5));
        }
	
	//find item sets of length one
	kSets = LabeledItemSet.singletons(m_instances,m_onlyClass);
	LabeledItemSet.upDateCounters(kSets, m_instances,m_onlyClass);
        
        //check if a item set of lentgh one is frequent, if not delete it
	kSets = LabeledItemSet.deleteItemSets(kSets, necSupport, m_instances.numInstances());
        if (kSets.size() == 0)
	    return;
	do {
	    m_Ls.addElement(kSets);
	    kMinusOneSets = kSets;
	    kSets = LabeledItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances());
	    hashtable = LabeledItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size());
	    kSets = LabeledItemSet.pruneItemSets(kSets, hashtable);
	    LabeledItemSet.upDateCounters(kSets, m_instances,m_onlyClass);
	    kSets = LabeledItemSet.deleteItemSets(kSets, necSupport, m_instances.numInstances());
	    i++;
	} while (kSets.size() > 0);
    } 

   

  /** 
   * Method that finds all class association rules.
   *
   * @throws Exception if an attribute is numeric
   */
   private void findCarRulesQuickly() throws Exception {

    FastVector[] rules;

    // Build rules
    for (int j = 0; j < m_Ls.size(); j++) {
      FastVector currentLabeledItemSets = (FastVector)m_Ls.elementAt(j);
      Enumeration enumLabeledItemSets = currentLabeledItemSets.elements();
      while (enumLabeledItemSets.hasMoreElements()) {
	LabeledItemSet currentLabeledItemSet = (LabeledItemSet)enumLabeledItemSets.nextElement();
	rules = currentLabeledItemSet.generateRules(m_minMetric,false);
	for (int k = 0; k < rules[0].size(); k++) {
	  m_allTheRules[0].addElement(rules[0].elementAt(k));
	  m_allTheRules[1].addElement(rules[1].elementAt(k));
	  m_allTheRules[2].addElement(rules[2].elementAt(k));
	}
      }
    }
  }

  /**
   * returns all the rules
   *
   * @return		all the rules
   * @see		#m_allTheRules
   */
  public FastVector[] getAllTheRules() {
    return m_allTheRules;
  }
  
  public AssociationRules getAssociationRules() {
    List<AssociationRule> rules = new ArrayList<AssociationRule>();
    
    if (m_allTheRules != null && m_allTheRules.length > 3) {
      for (int i = 0 ; i < m_allTheRules[0].size(); i++) {
        // Construct the Lists for the premise and consequence
        List<Item> premise = new ArrayList<Item>();
        List<Item> consequence = new ArrayList<Item>();
        
        AprioriItemSet premiseSet = (AprioriItemSet)m_allTheRules[0].get(i);
        AprioriItemSet consequenceSet = (AprioriItemSet)m_allTheRules[1].get(i);
        for (int j = 0; j < m_instances.numAttributes(); j++) {
          if (premiseSet.m_items[j] != -1) {
            try {
              Item newItem = new NominalItem(m_instances.attribute(j), premiseSet.m_items[j]);
              premise.add(newItem);
            } catch (Exception ex) {
              ex.printStackTrace();
            }
          }
          
          if (consequenceSet.m_items[j] != -1) {
            try {
              Item newItem = new NominalItem(m_instances.attribute(j), consequenceSet.m_items[j]);
              consequence.add(newItem);
            } catch (Exception ex) {
              ex.printStackTrace();
            }
          }
        }
        
        // get the constituents of the metrics
        int totalTrans = premiseSet.m_totalTransactions;
        int totalSupport = consequenceSet.m_counter;
        int premiseSupport = premiseSet.m_counter;
        // reconstruct consequenceSupport using Lift:
        double lift = ((Double)m_allTheRules[3].get(i)).doubleValue();
        double conf = ((Double)m_allTheRules[2].get(i)).doubleValue();
        int consequenceSupport = (int)((totalTrans * conf) / lift);
        
        // map the primary metric
        DefaultAssociationRule.METRIC_TYPE metric = null;
        switch(m_metricType) {
        case CONFIDENCE:
          metric = DefaultAssociationRule.METRIC_TYPE.CONFIDENCE;
          break;
        case LIFT:
          metric = DefaultAssociationRule.METRIC_TYPE.LIFT;
          break;
        case LEVERAGE:
          metric = DefaultAssociationRule.METRIC_TYPE.LEVERAGE;
          break;
        case CONVICTION:
          metric = DefaultAssociationRule.METRIC_TYPE.CONVICTION;
          break;
        }
        
        DefaultAssociationRule newRule = 
          new DefaultAssociationRule(premise, consequence, metric, 
              premiseSupport, consequenceSupport, totalSupport, totalTrans);
        
        rules.add(newRule);
      }
    }
    
    return new AssociationRules(rules, this);
  }
  
  /**
   * Gets a list of the names of the metrics output for
   * each rule. This list should be the same (in terms of
   * the names and order thereof) as that produced by
   * AssociationRule.getMetricNamesForRule().
   * 
   * @return an array of the names of the metrics available
   * for each rule learned by this producer.
   */
  public String[] getRuleMetricNames() {
    String[] metricNames = new String[DefaultAssociationRule.TAGS_SELECTION.length];
    
    for (int i = 0; i < DefaultAssociationRule.TAGS_SELECTION.length; i++) {
      metricNames[i] = DefaultAssociationRule.TAGS_SELECTION[i].getReadable();
    }
    
    return metricNames;
  }
  
  /**
   * Returns true if this AssociationRulesProducer can actually
   * produce rules. Most implementing classes will always return
   * true from this method (obviously :-)). However, an implementing
   * class that actually acts as a wrapper around things that may
   * or may not implement AssociationRulesProducer will want to
   * return false if the thing they wrap can't produce rules.
   * 
   * @return true if this producer can produce rules in its current
   * configuration
   */
  public boolean canProduceRules() {
    return true;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 7476 $");
  }

  /**
   * Main method.
   * 
   * @param args the commandline options
   */
  public static void main(String[] args) {
    runAssociator(new Apriori(), args);
  }
}

